Classification of PICO elements by text features systematically extracted from PubMed abstracts

Ke Chun Huang; Charles Chih Ho Liu; Shung Shiang Yang; Furen Xiao; Jau Min Wong; Chun Chih Liao; I. Jen Chiang

doi:10.1109/GRC.2011.6122608

Classification of PICO elements by text features systematically extracted from PubMed abstracts

Ke Chun Huang, Charles Chih Ho Liu, Shung Shiang Yang, Furen Xiao, Jau Min Wong, Chun Chih Liao, I. Jen Chiang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

We propose and evaluate a systematic approach to detect and classify Patient/Problem, Intervention, Comparison and Outcome (PICO) from the medical literature. The training and test corpora were generated systematically and automatically from structured PubMed abstracts. 23,472 sentences by exact pattern match of head words of P-I-O categories. Afterward, the terms with top frequencies were used as the features of Naïve Bayesian classifier. This approach achieves F-measure values of 0.91 for Patient/Problem, 0.75 for Intervention and 0.88 for Outcome, comparable to previous studied based on mixed textural, paragraphical, and semantic features. In conclusion, we show that by stricter pattern matching criteria of training set, detection and classification of PICO elements can be reproducible with minimal expert intervention. The results of this work are higher than previous studies.

Original language	English
Title of host publication	Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011
Pages	279-283
Number of pages	5
DOIs	https://doi.org/10.1109/GRC.2011.6122608
Publication status	Published - 2011
Event	2011 IEEE International Conference on Granular Computing, GrC 2011 - Kaohsiung, Taiwan Duration: Nov 8 2011 → Nov 10 2011

Other

Other	2011 IEEE International Conference on Granular Computing, GrC 2011
Country	Taiwan
City	Kaohsiung
Period	11/8/11 → 11/10/11

Keywords

information extraction
natural language processing
question answering
text mining

ASJC Scopus subject areas

Software

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10.1109/GRC.2011.6122608

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Classification of PICO elements by text features systematically extracted from PubMed abstracts. / Huang, Ke Chun; Liu, Charles Chih Ho; Yang, Shung Shiang; Xiao, Furen; Wong, Jau Min; Liao, Chun Chih; Chiang, I. Jen.

Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011. 2011. p. 279-283 6122608.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Huang, KC, Liu, CCH, Yang, SS, Xiao, F, Wong, JM, Liao, CC & Chiang, IJ 2011, Classification of PICO elements by text features systematically extracted from PubMed abstracts. in Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011., 6122608, pp. 279-283, 2011 IEEE International Conference on Granular Computing, GrC 2011, Kaohsiung, Taiwan, 11/8/11. https://doi.org/10.1109/GRC.2011.6122608

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title = "Classification of PICO elements by text features systematically extracted from PubMed abstracts",

abstract = "We propose and evaluate a systematic approach to detect and classify Patient/Problem, Intervention, Comparison and Outcome (PICO) from the medical literature. The training and test corpora were generated systematically and automatically from structured PubMed abstracts. 23,472 sentences by exact pattern match of head words of P-I-O categories. Afterward, the terms with top frequencies were used as the features of Na{\"i}ve Bayesian classifier. This approach achieves F-measure values of 0.91 for Patient/Problem, 0.75 for Intervention and 0.88 for Outcome, comparable to previous studied based on mixed textural, paragraphical, and semantic features. In conclusion, we show that by stricter pattern matching criteria of training set, detection and classification of PICO elements can be reproducible with minimal expert intervention. The results of this work are higher than previous studies.",

keywords = "information extraction, natural language processing, question answering, text mining",

author = "Huang, {Ke Chun} and Liu, {Charles Chih Ho} and Yang, {Shung Shiang} and Furen Xiao and Wong, {Jau Min} and Liao, {Chun Chih} and Chiang, {I. Jen}",

year = "2011",

doi = "10.1109/GRC.2011.6122608",

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AU - Wong, Jau Min

AU - Liao, Chun Chih

AU - Chiang, I. Jen

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N2 - We propose and evaluate a systematic approach to detect and classify Patient/Problem, Intervention, Comparison and Outcome (PICO) from the medical literature. The training and test corpora were generated systematically and automatically from structured PubMed abstracts. 23,472 sentences by exact pattern match of head words of P-I-O categories. Afterward, the terms with top frequencies were used as the features of Naïve Bayesian classifier. This approach achieves F-measure values of 0.91 for Patient/Problem, 0.75 for Intervention and 0.88 for Outcome, comparable to previous studied based on mixed textural, paragraphical, and semantic features. In conclusion, we show that by stricter pattern matching criteria of training set, detection and classification of PICO elements can be reproducible with minimal expert intervention. The results of this work are higher than previous studies.

AB - We propose and evaluate a systematic approach to detect and classify Patient/Problem, Intervention, Comparison and Outcome (PICO) from the medical literature. The training and test corpora were generated systematically and automatically from structured PubMed abstracts. 23,472 sentences by exact pattern match of head words of P-I-O categories. Afterward, the terms with top frequencies were used as the features of Naïve Bayesian classifier. This approach achieves F-measure values of 0.91 for Patient/Problem, 0.75 for Intervention and 0.88 for Outcome, comparable to previous studied based on mixed textural, paragraphical, and semantic features. In conclusion, we show that by stricter pattern matching criteria of training set, detection and classification of PICO elements can be reproducible with minimal expert intervention. The results of this work are higher than previous studies.

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Classification of PICO elements by text features systematically extracted from PubMed abstracts

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